Aesthetics (encodings)

PH345: Winter 2025

Phil Boonstra

Farbtafel, Paul Klee (1930)

Which color appears most often?

https://www.demilked.com/tidying-up-art-ursus-wehrli/

Tidied up Farbtafel, Ursus Wehrli (2003)

Which color appears most often?

https://www.demilked.com/tidying-up-art-ursus-wehrli/

Mapping data to aesthetics

Aesthetics or encodings are ways that we map data to visual properties of the plot and include position, color, length, shape, area, volume

Choice of aesthetics helps or hinders your audience’s understanding of what the data are showing

Example: five proportions

One proportion for each of five groups (A-E)

For each of 9 plots, guess group B’s numerical proportion and rank. Enter your guesses on this google form:

Plot 1

Plot 2

Plot 3

Plot 4

Plot 5

Plot 6

Plot 7

Plot 8

Plot 9

True values

Plot True value B True rank B
1 0.160 3
2 0.205 3
3 0.333 5
4 0.365 5
5 0.254 4
6 0.161 2
7 0.283 4
8 0.034 1
9 0.098 2

Relative order of accuracy

Take away: some aesthetics communicate data better than others

Figure 14 from Mackinlay (1986)

Bang Wong

Sr. Director at Vertex Pharmaceuticals.

Formerly Creative Director of the Broad Institute of MIT and adjunct assistant professor in the Department of Art as Applied to Medicine at Hopkins

Published monthly column on data visualization in Nature Methods journal from 2010-2012

Example 1

Different visual variables encoding the same five values.

Figure 1c from Wong (2010a)

Example 2

What is the rate of change of atmospheric CO2 over time?

Figure 6 from Cleveland and McGill (1985)

Example 3

What is the relative size of big vs small circle?

14x

How does distance between lines vary?

it’s constant

Figure 1c from Wong (2010a)

Types of data

  • Quantitative: numbers that measure units, e.g. years, kg, etc. Differences between numbers have meaning
  • Ordinal: numbers or categories that have natural order, e.g. Likert scales, tumor stage. Distances between numbers do not have consistent meaning (‘Almost always’ - ‘Sometimes’ = ?)
  • Nominal: Categories that have no inherent order, e.g. US states

Aesthetics for different types of data

Figure 15 from Mackinlay (1986)

Example 4

Lines in graphs create clear connection. Enclosure is an effective way to draw attention to a group of objects.

Figure 2b from Wong (2010b)

Example 5

What regions of the US experienced greatest population growth?

Figure 4.2, Wilke (2019)

References

Cleveland, W.S. and McGill, R., 1985. Graphical perception and graphical methods for analyzing scientific data. Science, 229(4716), pp.828-833.

Mackinlay, J., 1986. Automating the design of graphical presentations of relational information. Acm Transactions On Graphics (Tog), 5(2), pp.110-141.

Wehrli, U., 2003. Tidying Up Art. Prestel Publishing.

Wilke, C.O., 2019. Fundamentals of data visualization: a primer on making informative and compelling figures. O’Reilly Media.

Wong, B., 2010a. Design of data figures. Nature Methods, 7(9), pp.665-666.

Wong, B., 2010b. Points of view: Gestalt principles (Part 1). Nature Methods, 7(11), p.863.